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PVEIN-MLELM: a novel palm vein identification approach through multilayer extreme learning machine

dc.contributor.authorZabala-Blanco, David
dc.contributor.authorHernández-García, Ruber
dc.contributor.authorBarrientos, Jaime
dc.contributor.authorAhumada García, Roberto
dc.date.accessioned2023-03-22T17:36:16Z
dc.date.available2023-03-22T17:36:16Z
dc.date.issued2022
dc.identifier.urihttp://repositorio.ucm.cl/handle/ucm/4545
dc.description.abstractBiometric identification systems play an essential role in multiple application areas, such as banking services, e-government, and public security, among others. Particularly, palm vein recognition is considered an emerging technology from the last decade, avoiding forgery possibilities and presenting high identification reliability and accuracy. State-of-the-art in palm vein recognition has improved its results in recent years from different approaches based on deep learning. Some methods based on convolutional neural networks reported in the literature have achieved high recognition rates in public databases. However, computational simplicity and generalization capability are limited given the small number of samples in the databases. This paper introduces a model called PVEIN-MLELM based on the Multilayer Extreme Learning Machine (ML-ELM) for identifying persons through palm vein images. The ML-ELM algorithm offers advantages in terms of computational simplicity and speed of the training process while maintaining its generalization capability. Experimental results on four public datasets show recognition rates comparable to the state-of-the-art approaches while reducing memory requirements and significantly speeding up computational time.es_CL
dc.language.isoenes_CL
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 Chile*
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 Chile*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/cl/*
dc.sourceInternational Conference on Automation/XXV Congress of the Chilean Association of Automatic Control (ICA-ACCA), Curicó, Chile, 1-6es_CL
dc.subjectTraininges_CL
dc.subjectExtreme learning machineses_CL
dc.subjectDatabaseses_CL
dc.subjectBiometrics (access control)es_CL
dc.subjectVeinses_CL
dc.subjectNonhomogeneous mediaes_CL
dc.subjectSafetyes_CL
dc.titlePVEIN-MLELM: a novel palm vein identification approach through multilayer extreme learning machinees_CL
dc.typeArticlees_CL
dc.ucm.facultadFacultad de Ciencias de la Ingenieríaes_CL
dc.ucm.indexacionScopuses_CL
dc.ucm.uriieeexplore.ieee.org/document/10006171es_CL
dc.ucm.doidoi.org/10.1109/ICA-ACCA56767.2022.10006171es_CL


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Atribución-NoComercial-SinDerivadas 3.0 Chile
Excepto si se señala otra cosa, la licencia de la publicación se describe como Atribución-NoComercial-SinDerivadas 3.0 Chile